The present invention relates generally to document retrieval systems, and more particularly to a document retrieval system which allows a user to retrieve relevant documents being aligned in sequential order of a document accuracy, so that the user can modify the order of the aligned documents by giving instructions to the system for achieving a flexible, speedy document retrieval.
In conventional document retrieval systems, a registration operator gives inputs of appropriate keywords when a document is newly registered in the document retrieval system, and registers the document with such keywords. When retrieving a document, a user selects suitable keywords from a thesaurus and attempts a retrieval in the document retrieval system. Such a method is capable of performing a speedy retrieval, but there are some problems including, for example, the suitability of the keywords selected by the registration operator, the burdensome task of keyword classification and updating with the thesaurus, the need to retry finding any documents which nearly meet but do not fully satisfy a query given by the user, etc.
One method for eliminating such kinds of problems is disclosed, for example, in "Generation of Descriptor Relations of a Database Based on Fuzzy Sets and Application to Information Retrieval", a published article by T. Miyake et al. at the 4th Fuzzy System Symposium held on May 30-31, 1988 in Tokyo, Japan. In the method shown in this published article, a numerical expression is used to represent the relationships among keywords. However, this published article does not deal with a learning function and the proposed method is not adequate for a document retrieval system for practical use.
For the purpose of eliminating the drawbacks of the prior art described above, a document retrieval system is proposed by the same applicant. Using a numerical expression of keyword connections representing relationships among keywords, a document accuracy is adapted to this conventional document retrieval system. The document accuracy is obtained by the document retrieval system with a value of a keyword connection which becomes greater as the contents of the searched document become closer to what is sought by the user. This enables the user to find out a more closely related document in a flexible manner among those which meet the user's requests.
The prior art system discussed above employs a keyword connection which is a measure of indicating a degree of relations among keywords. The document accuracy is calculated from a set of keywords indexed to the registered documents in a database file of the system and from the query containing a set of keywords, and the document accuracy for each document is displayed. The more approximate to the query the contents of each document are, the greater the document accuracy becomes. However, this prior art system has no learning function which allows the subsequent retrieval results to reflect modifications made by the user with respect to the previous retrieval results.
In addition, an improved document retrieval system is proposed by the same applicant. This document retrieval system has a learning function, that is, the system receives a value from the user who makes a decision on whether each of the output documents are in conformity with what is initially sought by the user, then the system modifies the weight assigned to each of the keyword connections in response to the value, and this document retrieval system therefore allows the subsequent retrieval results to reflect the value previously given by the user. In the conventional document retrieval system discussed above, an evaluation function is introduced which helps describe the difference of the document accuracy of each document searched from the value given by the user with a binary expression of one or zero. The value is given by the user with the binary expression of one or zero, that is, "one" meaning that the searched document is in conformity with the query and "zero" indicating that it is not in conformity. And this document retrieval system has a learning function which serves to make a difference obtained from the evaluation function smaller. However, this conventional system has difficulty handling an ambiguous decision by the user because the user has no choice other than the two inputs of one and zero when making an evaluation on whether the previous outputs from the system are in conformity with the query.
Generally, the user does not necessarily have a clear answer regarding the suitability of the searched documents, and so the user is often unable to immediately make a yes-or-no decision on whether an output document is in conformity with his request. It is difficult for the prior art document retrieval system to provide the user with the ability to make flexible choices when the user seeks to give such a vague input to the system.